# Contrastive learning for video reuse detection

arXiv: https://arxiv.org/abs/2610.17098 · HTML: https://mlsanity.com/paper/2610.17098/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 13 of its 28 references that are in the map.
- First posted on arXiv 12 Oct 2026.

## Builds on (most cited first, 13 of 13)

- [Deep Residual Learning for Image Recognition](https://mlsanity.com/paper/1512.03385/) (2015)
- [Learning Transferable Visual Models From Natural Language Supervision](https://mlsanity.com/paper/2103.00020/) (2021)
- [A Simple Framework for Contrastive Learning of Visual Representations](https://mlsanity.com/paper/2002.05709/) (2020)
- [Densely Connected Convolutional Networks](https://mlsanity.com/paper/1608.06993/) (2016)
- [BLIP-2: Bootstrapping Language-Image Pre-training with Frozen Image Encoders and Large Language Models](https://mlsanity.com/paper/2301.12597/) (2023)
- [Adaptive Keyframe Sampling for Long Video Understanding](https://mlsanity.com/paper/2502.21271/) (2025)
- [SIFT Meets CNN: A Decade Survey of Instance Retrieval](https://mlsanity.com/paper/1608.01807/) (2016)
- [Generalizing Deepfake Video Detection with Plug-and-Play: Video-Level Blending and Spatiotemporal Adapter Tuning](https://mlsanity.com/paper/2408.17065/) (2024)
- [On Semantic Similarity in Video Retrieval](https://mlsanity.com/paper/2103.10095/) (2021)
- [Video Contrastive Learning with Global Context](https://mlsanity.com/paper/2108.02722/) (2021)
- [Multimodal Content Analysis for Effective Advertisements on YouTube](https://mlsanity.com/paper/1709.03946/) (2017)
- [VITR: Augmenting Vision Transformers with Relation-Focused Learning for Cross-Modal Information Retrieval](https://mlsanity.com/paper/2302.06350/) (2023)
- [Evaluating AI capabilities in detecting conspiracy theories on YouTube](https://mlsanity.com/paper/2505.23570/) (2025)

## Abstract (arXiv)

The incorporation of footage from other sources has long been a concern from a copyright perspective, as such usage can harm the interests of rights holders. Recent trends in the ``remixing'' of social media content and the development of bespoke stock libraries for specialist causes mean that methods to identify content reuse will have a range of applications. In this paper, we develop a framework based on a Siamese network fine tuned using contrastive learning to identify potential sources for key frames in a given video. This framework makes two contributions to analysis of large video datasets. Firstly, we demonstrate its utility at reducing complex video data to a set of key visuals typically less than 10% of the size of the full set. Secondly, we show that these reduced sets allow for efficient and effective identification of component footage. We test this method over a set of videos compiled from a stock footage library and demonstrate its effectiveness for constituent video identification with average accuracy greater than 0.95.